In hierarchical databases, dimensions often have a hierarchical structure. For example, a dimension “products” in an OLAP (On-Line Analytical Processing) cube may itself have several members such as “accessories,” “bikes,” “clothing,” etc. Each of those lower-level dimensions may in turn have members. The hierarchical structure may be visualized as a tree having multiple levels that forms a set of parent-child relationships between the nodes of the tree. These relationships are the basis for aggregation, as well as expand and collapse operations within the dimension hierarchy. The highest level of the hierarchy is the most aggregated and the lowest level is the least aggregated. Each level corresponds to a different semantic level of detail for that dimension.
On-Line Analytical Processing (OLAP) is an approach that allows decision-makers to quickly and interactively analyze multi-dimensionally modeled data appropriate to various contexts. Example applications of OLAP include business reporting for sales, marketing, management reporting, business process management, budget forecasting, financial reporting, and similar areas.